Auto-completion for Gesture-input in Assistant Systems

ABSTRACT

In one embodiment, a method includes detecting a user input comprising an incomplete gesture performed by one or more hands of a first user by a client system associated with the first user; selecting one or more candidate gestures from a plurality of pre-defined gestures by the client system based on a personalized gesture-recognition model, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate gestures at the client system.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. PatentApplication No. 16/389708, filed 19 Apr. 2019, which claims the benefit,under 35 U.S.C. § 119(e), of U.S. Provisional Patent Application No.62/660876, filed 20 Apr. 2018, each of which is incorporated herein byreference.

TECHNICAL FIELD

This disclosure generally relates to dialog management based onmachine-learning techniques within network environments, and inparticular relates to hardware and software for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in the social-networking system a user profile associated with the user. The userprofile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. The assistant system may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system may analyze theuser input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may use dialogmanagement techniques to manage and forward the conversation flow withthe user. In particular embodiments, the assistant system may furtherassist the user to effectively and efficiently digest the obtainedinformation by summarizing the information. The assistant system mayalso assist the user to be more engaging with an online social networkby providing tools that help the user interact with the online socialnetwork (e.g., creating posts, comments, messages). The assistant systemmay additionally assist the user to manage different tasks such askeeping track of events. In particular embodiments, the assistant systemmay proactively execute tasks that are relevant to user interests andpreferences based on the user profile without a user input. Inparticular embodiments, the assistant system may check privacy settingsto ensure that accessing a user's profile or other user information andexecuting different tasks are permitted subject to the user's privacysettings.

In particular embodiments, the assistant system may receive an initialinput in a first modality executed by a user as an interaction with anassistant-based client system, e.g., a gesture-input to a virtualreality (VR) headset or augment reality (AR) smart glasses, determinecandidate continuation-inputs in an auto-completion manner based on theinitial input, and recommend these candidate continuation-inputs to theuser in one or more second modalities (e.g., text), for which the usermay select one of them to trigger the execution of a particular task.The initial input and continuation-inputs may be based on any suitablemodality including text, speech, image, video, motion, orientation, etc.In addition, the modality of the initial input and the modalities of thecontinuation-inputs may be different. As an example and not by way oflimitation, a user visiting Paris who wears AR glasses may be looking atEiffel Tower. Based on the gaze input, the assistant system may suggesta gesture displayed on the virtual screen for taking a picture orsuggest the user to speak “take a picture.” The user may perform thesuggested gesture or speak the sentence to allow the assistant system toexecute the task of taking a picture of Eiffel Tower. As a result, theassistant system may have the ability to process a user input in onemodality and generate suggested inputs in one or more other modalities,which may be referred as auto-completion for multi-modal user input.Although this disclosure describes generating particular auto-completionfor particular multi-modal user inputs via particular systems inparticular manners, this disclosure contemplates generating any suitableauto-completion for any suitable multi-modal user input via any suitablesystem in any suitable manner.

In particular embodiments, the assistant system may receive, from aclient system associated with a first user, an initial input from thefirst user. The initial input may be in a first modality. In particularembodiments, the assistant system may determine, by anintent-understanding module, one or more intents corresponding to theinitial input. The assistant system may then generate, based on the oneor more intents, one or more candidate continuation-inputs. The one ormore candidate continuation-inputs may be in one or more candidatemodalities, respectively. The candidate modalities may be different fromthe first modality. In particular embodiments, the assistant system mayfurther send, to the client system, instructions for presenting one ormore suggested inputs corresponding to one or more of the candidatecontinuation-inputs.

In particular embodiments, the assistant system may receive anincomplete gesture executed by a user as an interaction with anassistant-based client system, e.g., a VR headset or AR smart glasses,determine candidate gestures in an auto-completion manner based on theincomplete gesture, recommend these candidate gestures to the user, forwhich the user may select one of them to trigger the execution of aparticular task. As an example and not by way of limitation, a userwearing AR glasses may not know what gesture to perform to trigger aparticular function of the assistant system. The user may start movinghis/her hand but pause in the air. Accordingly, the assistant system mayanalyze the incomplete gesture of the user and determine possiblecandidate gestures. The assistant system may display these candidategestures visually via the AR glasses to the user. After the user selectsone of them, the assistant system may further execute a taskcorresponding to that gesture. In summary, the assistant system may usegesture recognition techniques on incomplete gestures to determine theuser-intended gestures and suggest the determined gestures as guidanceto the user, which may be referred as auto-completion for gesture-input.Although this disclosure describes generating particular auto-completionfor particular gesture-inputs via particular systems in particularmanners, this disclosure contemplates generating any suitableauto-completion for any suitable gesture-input via any suitable systemin any suitable manner.

In particular embodiments, the assistant system may receive, from aclient system associated with a first user, a user input from the firstuser. The user input may comprise an incomplete gesture performed by thefirst user. In particular embodiments, the assistant system maycalculate, by an intent-understanding module, one or more confidencesscores for one or more intents corresponding to the incomplete gesture.The assistant system may then determine that the calculated confidencescores associated with each of the intents are below a threshold score.In particular embodiments, the assistant system may select, based on apersonalized gesture-recognition model, one or more candidate gesturesfrom a plurality of pre-defined gestures responsive to determining thatthe calculated confidence scores for each of the intents are below thethreshold score. Each of the candidate gestures may be associated with aconfidence score representing a likelihood the first user intended toinput the respective candidate gesture. In particular embodiments, theassistant system may further send, to the client system, instructionsfor presenting one or more suggested inputs corresponding to one or moreof the candidate gestures.

Certain technical challenges exist for achieving the goal of suggestingmulti-modal user input for auto-completion. One technical challenge mayinclude determining candidate continuation-inputs based on the initialinput. A solution presented by the embodiments disclosed herein toaddress the above challenge is determining the candidatecontinuation-inputs based on the intent of the initial input and thepotential entities relevant to the intent, or an object associated withthe initial input, as such information may be necessary to determinewhat continuation-input a user may use to interact with the assistantsystem to execute tasks that based on the intent and the entities or theobject. Another technical challenge may include determining that a userneeds suggested inputs. A solution presented by the embodimentsdisclosed herein to address this challenge is determining whether tosuggest candidate continuation-inputs based on different factorsincluding wake-up inputs, gaze information, contextual information, andintents, as such information may reveal a user's requirement forassistance from different perspectives.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeenriching user experience with the assistant system as a user mayinteract with the assistant system with inputs in various modalities.Another technical advantage of the embodiments may include increasingthe degree of users engaging with the assistant system by guiding userswith sequences of suggested inputs to explore various tasks that theusers may request assistance. Certain embodiments disclosed herein mayprovide none, some, or all of the above technical advantages. One ormore other technical advantages may be readily apparent to one skilledin the art in view of the figures, descriptions, and claims of thepresent disclosure.

Certain technical challenges exist for achieving the goal of suggestingcandidate gestures for auto-completion. One technical challenge includesdetermining similarity levels of candidate gestures with respect to theincomplete gesture. The solution presented by the embodiments disclosedherein to address the above challenge is determining the similaritylevels based on different factors including trajectory, orientation,objects, contextual information, and position associated with theincomplete gesture, as such information may provide differentinformative cues for the assistant system to calculate similaritylevels. Another technical challenge includes determining that a userneeds suggested inputs for gestures. The solution presented by theembodiments disclosed herein to address this challenge is determiningwhether to suggest candidate gestures based on different factorsincluding confidence scores of intents, wake-up gestures, temporalinformation of the incomplete gesture, velocity of the incompletegesture, and patterns of the incomplete gesture, as such information mayreveal a user's requirement for assistance from different perspectives.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeimproving user experience with the assistant system as the assistantsystem may understand a user's intent based on an incomplete gesture andteach a user to complete the incomplete gesture to interact with theassistant system. Another technical advantage of the embodiments mayinclude increasing the degree of users engaging with the assistantsystem by guiding users with sequences of suggested gestures to explorevarious tasks that the users may request assistance. Certain embodimentsdisclosed herein may provide none, some, or all of the above technicaladvantages. One or more other technical advantages may be readilyapparent to one skilled in the art in view of the figures, descriptions,and claims of the present disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system.

FIG. 4 illustrates an example workflow of processing a user inputcomprising a gesture-input.

FIG. 5 illustrates an example scenario of auto-completion formulti-modal user input.

FIG. 6 illustrates an example scenario of auto-completion forgesture-input.

FIG. 7A illustrates an example scenario of an incomplete gesture inAR/VR setting.

FIG. 7B illustrates an example scenario of suggested gesture-input inAR/VR setting.

FIG. 8 illustrates an example method for suggesting multi-modal userinput for auto-completion.

FIG. 9 illustrates an example method for suggesting candidate gesturesfor auto-completion.

FIG. 10 illustrates an example social graph.

FIG. 11 illustrates an example view of an embedding space.

FIG. 12 illustrates an example artificial neural network.

FIG. 13 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949011, filed 9 Apr. 2018, U.S. patentapplication Ser. No. 62/655751, filed 10 Apr. 2018, U.S. Design PatentApplication No. 29/631910, filed 3 Jan. 2018, U.S. Design PatentApplication No. 29/631747, filed 2 Jan. 2018, U.S. Design PatentApplication No. 29/631913, filed 3 Jan. 2018, and U.S. Design PatentApplication No. 29/631914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, orientation, etc. The assistant application136 may communicate the user input to the assistant system 140. Based onthe user input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality). The assistant application 136 may thencommunicate the request to the assistant system 140. The assistantsystem 140 may accordingly generate the result and send it back to theassistant application 136. The assistant application 136 may furtherpresent the result to the user in text.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, an assistant system 140, or a third-party system 170 tomanage, retrieve, modify, add, or delete, the information stored in datastore 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow anassistant system 140 and a third-party system 170 to access informationfrom the social-networking system 160 by calling one or more APIs. Anaction logger may be used to receive communications from a web serverabout a user's actions on or off the social-networking system 160. Inconjunction with the action log, a third-party-content-object log may bemaintained of user exposures to third-party-content objects. Anotification controller may provide information regarding contentobjects to a client system 130. Information may be pushed to a clientsystem 130 as notifications, or information may be pulled from a clientsystem 130 responsive to a request received from a client system 130.Authorization servers may be used to enforce one or more privacysettings of the users of the social-networking system 160. A privacysetting of a user determines how particular information associated witha user can be shared. The authorization server may allow users to opt into or opt out of having their actions logged by the social-networkingsystem 160 or shared with other systems (e.g., a third-party system170), such as, for example, by setting appropriate privacy settings.Third-party-content-object stores may be used to store content objectsreceived from third parties, such as a third-party system 170. Locationstores may be used for storing location information received from clientsystems 130 associated with users. Advertisement-pricing modules maycombine social information, the current time, location information, orother suitable information to provide relevant advertisements, in theform of notifications, to a user.

Assistant Systems

FIG. 2 illustrates an example architecture of the assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video, motion) in stateful and multi-turn conversations toget assistance. The assistant system 140 may create and store a userprofile comprising both personal and contextual information associatedwith the user. In particular embodiments, the assistant system 140 mayanalyze the user input using natural-language understanding. Theanalysis may be based on the user profile for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation. Through the interaction with the user, theassistant system 140 may use dialog management techniques to manage andforward the conversation flow with the user. In particular embodiments,the assistant system 140 may further assist the user to effectively andefficiently digest the obtained information by summarizing theinformation. The assistant system 140 may also assist the user to bemore engaging with an online social network by providing tools that helpthe user interact with the online social network (e.g., creating posts,comments, messages). The assistant system 140 may additionally assistthe user to manage different tasks such as keeping track of events. Inparticular embodiments, the assistant system 140 may proactively executepre-authorized tasks that are relevant to user interests and preferencesbased on the user profile, at a time relevant for the user, without auser input. In particular embodiments, the assistant system 140 maycheck privacy settings to ensure that accessing a user's profile orother user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. patentapplication Ser. No. 62/675090, filed 22 May 2018, which is incorporatedby reference.

In particular embodiments, the assistant system 140 may receive a userinput from the assistant application 136 in the client system 130associated with the user. In particular embodiments, the user input maybe a user generated input that is sent to the assistant system 140 in asingle turn. If the user input is based on a text modality, theassistant system 140 may receive it at a messaging platform 205. If theuser input is based on an audio modality (e.g., the user may speak tothe assistant application 136 or send a video including speech to theassistant application 136), the assistant system 140 may process itusing an automatic speech recognition (ASR) module 210 to convert theuser input into text. If the user input is based on an image or videomodality, the assistant system 140 may process it using opticalcharacter recognition techniques within the messaging platform 205 toconvert the user input into text. The output of the messaging platform205 or the ASR module 210 may be received at an assistant xbot 215. Moreinformation on handling user input based on different modalities may befound in U.S. patent application Ser. No. 16/053600, filed 2 Aug. 2018,which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may be a type of chatbot. The assistant xbot 215 may comprise a programmable service channel,which may be a software code, logic, or routine that functions as apersonal assistant to the user. The assistant xbot 215 may work as theuser's portal to the assistant system 140. The assistant xbot 215 maytherefore be considered as a type of conversational agent. In particularembodiments, the assistant xbot 215 may send the textual user input to anatural-language understanding (NLU) module 220 to interpret the userinput. In particular embodiments, the NLU module 220 may get informationfrom a user context engine 225 and a semantic information aggregator(SIA) 230 to accurately understand the user input. The user contextengine 225 may store the user profile of the user. The user profile ofthe user may comprise user-profile data including demographicinformation, social information, and contextual information associatedwith the user. The user-profile data may also include user interests andpreferences on a plurality of topics, aggregated through conversationson news feed, search logs, messaging platform 205, etc. The usage of auser profile may be protected behind a privacy check module 245 toensure that a user's information can be used only for his/her benefit,and not shared with anyone else. More information on user profiles maybe found in U.S. patent application Ser. No. 15/967239, filed 30 Apr.2018, which is incorporated by reference. The semantic informationaggregator 230 may provide ontology data associated with a plurality ofpredefined domains, intents, and slots to the NLU module 220. Inparticular embodiments, a domain may denote a social context ofinteraction, e.g., education. An intent may be an element in apre-defined taxonomy of semantic intentions, which may indicate apurpose of a user interacting with the assistant system 140. Inparticular embodiments, an intent may be an output of the NLU module 220if the user input comprises a text/speech input. The NLU module 220 mayclassify the text/speech input into a member of the pre-definedtaxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 220may classify the input as having the intent [IN:play_music]. Inparticular embodiments, a domain may be conceptually a namespace for aset of intents, e.g., music. A slot may be a named sub-string with theuser input, representing a basic semantic entity. For example, a slotfor “pizza” may be [SL:dish]. In particular embodiments, a set of validor expected named slots may be conditioned on the classified intent. Asan example and not by way of limitation, for [IN:play_music], a slot maybe [SL:song_name]. The semantic information aggregator 230 mayadditionally extract information from a social graph, a knowledge graph,and a concept graph, and retrieve a user's profile from the user contextengine 225. The semantic information aggregator 230 may further processinformation from these different sources by determining what informationto aggregate, annotating n-grams of the user input, ranking the n-gramswith confidence scores based on the aggregated information, formulatingthe ranked n-grams into features that can be used by the NLU module 220for understanding the user input. More information on aggregatingsemantic information may be found in U.S. patent application Ser. No.15/967342, filed 30 Apr. 2018, which is incorporated by reference. Basedon the output of the user context engine 225 and the semanticinformation aggregator 230, the NLU module 220 may identify a domain, anintent, and one or more slots from the user input in a personalized andcontext-aware manner. As an example and not by way of limitation, a userinput may comprise “show me how to get to the coffee shop”. The NLUmodule 220 may identify the particular coffee shop that the user wantsto go based on the user's personal information and the associatedcontextual information. In particular embodiments, the NLU module 220may comprise a lexicon of language and a parser and grammar rules topartition sentences into an internal representation. The NLU module 220may also comprise one or more programs that perform naive semantics orstochastic semantic analysis to the use of pragmatics to understand auser input. In particular embodiments, the parser may be based on a deeplearning architecture comprising multiple long- short term memory (LSTM)networks. As an example and not by way of limitation, the parser may bebased on a recurrent neural network grammar (RNNG) model, which is atype of recurrent and recursive LSTM algorithm. More information onnatural-language understanding may be found in U.S. patent applicationSer. No. 16/011062, filed 18 Jun. 2018, U.S. patent application Ser. No.16/025317, filed 2 Jul. 2018, and U.S. Patent Application No. 16/038120,filed 17 Jul. 2018, each of which is incorporated by reference.

In particular embodiments, the identified domain, intent, and one ormore slots from the NLU module 220 may be sent to a dialog engine 235.In particular embodiments, the dialog engine 235 may manage the dialogstate and flow of the conversation between the user and the assistantxbot 215. The dialog engine 235 may additionally store previousconversations between the user and the assistant xbot 215. In particularembodiments, the dialog engine 235 may communicate with an entityresolution module 240 to resolve entities associated with the one ormore slots, which supports the dialog engine 235 to forward the flow ofthe conversation between the user and the assistant xbot 215. Inparticular embodiments, the entity resolution module 240 may access thesocial graph, the knowledge graph, and the concept graph when resolvingthe entities. Entities may include, for example, unique users orconcepts, each of which may have a unique identifier (ID). As an exampleand not by way of limitation, the knowledge graph may comprise aplurality of entities. Each entity may comprise a single recordassociated with one or more attribute values. The particular record maybe associated with a unique entity identifier. Each record may havediverse values for an attribute of the entity. Each attribute value maybe associated with a confidence probability. A confidence probabilityfor an attribute value represents a probability that the value isaccurate for the given attribute. Each attribute value may be alsoassociated with a semantic weight. A semantic weight for an attributevalue may represent how the value semantically appropriate for the givenattribute considering all the available information. For example, theknowledge graph may comprise an entity of a movie “The Martian” (2015),which includes information that has been extracted from multiple contentsources (e.g., social media, knowledge bases, movie review sources,media databases, and entertainment content sources), and then deduped,resolved, and fused to generate the single unique record for theknowledge graph. The entity may be associated with a space attributevalue which indicates the genre of the movie “The Martian” (2015). Moreinformation on the knowledge graph may be found in U.S. patentapplication Ser. No. 16/048049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048101, filed 27 Jul. 2018, each of which isincorporated by reference. The entity resolution module 240 mayadditionally request a user profile of the user associated with the userinput from the user context engine 225. In particular embodiments, theentity resolution module 240 may communicate with a privacy check module245 to guarantee that the resolving of the entities does not violateprivacy policies. In particular embodiments, the privacy check module245 may use an authorization/privacy server to enforce privacy policies.As an example and not by way of limitation, an entity to be resolved maybe another user who specifies in his/her privacy settings that his/heridentity should not be searchable on the online social network, and thusthe entity resolution module 240 may not return that user's identifierin response to a request. Based on the information obtained from thesocial graph, knowledge graph, concept graph, and user profile, andsubject to applicable privacy policies, the entity resolution module 240may therefore accurately resolve the entities associated with the userinput in a personalized and context-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID). In particular embodiments, each of theresolved entities may be also associated with a confidence score. Moreinformation on resolving entities may be found in U.S. patentapplication Ser. No. 16/048049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048072, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the dialog engine 235 may communicate withdifferent agents based on the identified intent and domain, and theresolved entities. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. As an example and not byway of limitation, multiple device-specific implementations (e.g.,real-time calls for a client system 130 or a messaging application onthe client system 130) may be handled internally by a single agent.Alternatively, these device-specific implementations may be handled bymultiple agents associated with multiple domains. In particularembodiments, the agents may comprise first-party agents 250 andthird-party agents 255. In particular embodiments, first-party agents250 may comprise internal agents that are accessible and controllable bythe assistant system 140 (e.g. agents associated with services providedby the online social network). In particular embodiments, third-partyagents 255 may comprise external agents that the assistant system 140has no control over (e.g., music streams agents, ticket sales agents).The first-party agents 250 may be associated with first-party providers260 that provide content objects and/or services hosted by thesocial-networking system 160. The third-party agents 255 may beassociated with third-party providers 265 that provide content objectsand/or services hosted by the third-party system 170.

In particular embodiments, the communication from the dialog engine 235to the first-party agents 250 may comprise requesting particular contentobjects and/or services provided by the first-party providers 260. As aresult, the first-party agents 250 may retrieve the requested contentobjects from the first-party providers 260 and/or execute tasks thatcommand the first-party providers 260 to perform the requested services.In particular embodiments, the communication from the dialog engine 235to the third-party agents 255 may comprise requesting particular contentobjects and/or services provided by the third-party providers 265. As aresult, the third-party agents 255 may retrieve the requested contentobjects from the third-party providers 265 and/or execute tasks thatcommand the third-party providers 265 to perform the requested services.The third-party agents 255 may access the privacy check module 245 toguarantee no privacy violations before interacting with the third-partyproviders 265. As an example and not by way of limitation, the userassociated with the user input may specify in his/her privacy settingsthat his/her profile information is invisible to any third-party contentproviders. Therefore, when retrieving content objects associated withthe user input from the third-party providers 265, the third-partyagents 255 may complete the retrieval without revealing to thethird-party providers 265 which user is requesting the content objects.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may retrieve a user profile from the user contextengine 225 to execute tasks in a personalized and context-aware manner.As an example and not by way of limitation, a user input may comprise“book me a ride to the airport.” A transportation agent may execute thetask of booking the ride. The transportation agent may retrieve the userprofile of the user from the user context engine 225 before booking theride. For example, the user profile may indicate that the user preferstaxis, so the transportation agent may book a taxi for the user. Asanother example, the contextual information associated with the userprofile may indicate that the user is in a hurry so the transportationagent may book a ride from a ride-sharing service for the user since itmay be faster to get a car from a ride-sharing service than a taxicompany. In particular embodiment, each of the first-party agents 250 orthird-party agents 255 may take into account other factors whenexecuting tasks. As an example and not by way of limitation, otherfactors may comprise price, rating, efficiency, partnerships with theonline social network, etc.

In particular embodiments, the dialog engine 235 may communicate with aconversational understanding composer (CU composer) 270. The dialogengine 235 may send the requested content objects and/or the statuses ofthe requested services to the CU composer 270. In particularembodiments, the dialog engine 235 may send the requested contentobjects and/or the statuses of the requested services as a <k, c, u,d >tuple, in which k indicates a knowledge source, c indicates acommunicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer 270 maycomprise a natural-language generator (NLG) 271 and a user interface(UI) payload generator 272. The natural-language generator 271 maygenerate a communication content based on the output of the dialogengine 235. In particular embodiments, the NLG 271 may comprise acontent determination component, a sentence planner, and a surfacerealization component. The content determination component may determinethe communication content based on the knowledge source, communicativegoal, and the user's expectations. As an example and not by way oflimitation, the determining may be based on a description logic. Thedescription logic may comprise, for example, three fundamental notionswhich are individuals (representing objects in the domain), concepts(describing sets of individuals), and roles (representing binaryrelations between individuals or concepts). The description logic may becharacterized by a set of constructors that allow the natural-languagegenerator 271 to build complex concepts/roles from atomic ones. Inparticular embodiments, the content determination component may performthe following tasks to determine the communication content. The firsttask may comprise a translation task, in which the input to thenatural-language generator 271 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by thenatural-language generator 271. The sentence planner may determine theorganization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator 272 may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer 270 may communicatewith the privacy check module 245 to make sure the generation of thecommunication content follows the privacy policies. In particularembodiments, the CU composer 270 may retrieve a user profile from theuser context engine 225 when generating the communication content anddetermining the modality of the communication content. As a result, thecommunication content may be more natural, personalized, andcontext-aware for the user. As an example and not by way of limitation,the user profile may indicate that the user likes short sentences inconversations so the generated communication content may be based onshort sentences. As another example and not by way of limitation, thecontextual information associated with the user profile may indicatedthat the user is using a device that only outputs audio signals so theUI payload generator 272 may determine the modality of the communicationcontent as audio. More information on natural-language generation may befound in U.S. patent application Ser. No. 15/967279, filed 30 Apr. 2018,and U.S. patent application Ser. No. 15/966455, filed 30 Apr. 2018, eachof which is incorporated by reference.

In particular embodiments, the CU composer 270 may send the generatedcommunication content to the assistant xbot 215. In particularembodiments, the assistant xbot 215 may send the communication contentto the messaging platform 205. The messaging platform 205 may furthersend the communication content to the client system 130 via theassistant application 136. In alternative embodiments, the assistantxbot 215 may send the communication content to a text-to-speech (TTS)module 275. The TTS module 275 may convert the communication content toan audio clip. The TTS module 275 may further send the audio clip to theclient system 130 via the assistant application 136.

In particular embodiments, the assistant xbot 215 may interact with aproactive inference layer 280 without receiving a user input. Theproactive inference layer 280 may infer user interests and preferencesbased on the user profile that is retrieved from the user context engine225. In particular embodiments, the proactive inference layer 280 mayfurther communicate with proactive agents 285 regarding the inference.The proactive agents 285 may execute proactive tasks based on theinference. As an example and not by way of limitation, the proactivetasks may comprise sending content objects or providing services to theuser. In particular embodiments, each proactive task may be associatedwith an agenda item. The agenda item may comprise a recurring item suchas a daily digest. The agenda item may also comprise a one-time item. Inparticular embodiments, a proactive agent 285 may retrieve the userprofile from the user context engine 225 when executing the proactivetask. Therefore, the proactive agent 285 may execute the proactive taskin a personalized and context-aware manner. As an example and not by wayof limitation, the proactive inference layer may infer that the userlikes the band Maroon 5 and the proactive agent 285 may generate arecommendation of Maroon 5's new song/album to the user.

In particular embodiments, the proactive agent 285 may generatecandidate entities associated with the proactive task based on a userprofile. The generation may be based on a straightforward backend queryusing deterministic filters to retrieve the candidate entities from astructured data store. The generation may be alternatively based on amachine-learning model that is trained based on the user profile, entityattributes, and relevance between users and entities. As an example andnot by way of limitation, the machine-learning model may be based onsupport vector machines (SVM). As another example and not by way oflimitation, the machine-learning model may be based on a regressionmodel. As another example and not by way of limitation, themachine-learning model may be based on a deep convolutional neuralnetwork (DCNN). In particular embodiments, the proactive agent 285 mayalso rank the generated candidate entities based on the user profile andthe content associated with the candidate entities. The ranking may bebased on the similarities between a user's interests and the candidateentities. As an example and not by way of limitation, the assistantsystem 140 may generate a feature vector representing a user's interestand feature vectors representing the candidate entities. The assistantsystem 140 may then calculate similarity scores (e.g., based on cosinesimilarity) between the feature vector representing the user's interestand the feature vectors representing the candidate entities. The rankingmay be alternatively based on a ranking model that is trained based onuser feedback data.

In particular embodiments, the proactive task may comprise recommendingthe candidate entities to a user. The proactive agent 285 may schedulethe recommendation, thereby associating a recommendation time with therecommended candidate entities. The recommended candidate entities maybe also associated with a priority and an expiration time. In particularembodiments, the recommended candidate entities may be sent to aproactive scheduler. The proactive scheduler may determine an actualtime to send the recommended candidate entities to the user based on thepriority associated with the task and other relevant factors (e.g.,clicks and impressions of the recommended candidate entities). Inparticular embodiments, the proactive scheduler may then send therecommended candidate entities with the determined actual time to anasynchronous tier. The asynchronous tier may temporarily store therecommended candidate entities as a job. In particular embodiments, theasynchronous tier may send the job to the dialog engine 235 at thedetermined actual time for execution. In alternative embodiments, theasynchronous tier may execute the job by sending it to other surfaces(e.g., other notification services associated with the social-networkingsystem 160). In particular embodiments, the dialog engine 235 mayidentify the dialog intent, state, and history associated with the user.Based on the dialog intent, the dialog engine 235 may select somecandidate entities among the recommended candidate entities to send tothe client system 130. In particular embodiments, the dialog state andhistory may indicate if the user is engaged in an ongoing conversationwith the assistant xbot 215. If the user is engaged in an ongoingconversation and the priority of the task of recommendation is low, thedialog engine 235 may communicate with the proactive scheduler toreschedule a time to send the selected candidate entities to the clientsystem 130. If the user is engaged in an ongoing conversation and thepriority of the task of recommendation is high, the dialog engine 235may initiate a new dialog session with the user in which the selectedcandidate entities may be presented. As a result, the interruption ofthe ongoing conversation may be prevented. When it is determined thatsending the selected candidate entities is not interruptive to the user,the dialog engine 235 may send the selected candidate entities to the CUcomposer 270 to generate a personalized and context-aware communicationcontent comprising the selected candidate entities, subject to theuser's privacy settings. In particular embodiments, the CU composer 270may send the communication content to the assistant xbot 215 which maythen send it to the client system 130 via the messaging platform 205 orthe TTS module 275. More information on proactively assisting users maybe found in U.S. patent application Ser. No. 15/967193, filed 30 Apr.2018, and U.S. patent application Ser. No. 16/036827, filed 16 Jul.2018, each of which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may communicate with aproactive agent 285 in response to a user input. As an example and notby way of limitation, the user may ask the assistant xbot 215 to set upa reminder. The assistant xbot 215 may request a proactive agent 285 toset up such reminder and the proactive agent 285 may proactively executethe task of reminding the user at a later time.

In particular embodiments, the assistant system 140 may comprise asummarizer 290. The summarizer 290 may provide customized news feedsummaries to a user. In particular embodiments, the summarizer 290 maycomprise a plurality of meta agents. The plurality of meta agents mayuse the first-party agents 250, third-party agents 255, or proactiveagents 285 to generated news feed summaries. In particular embodiments,the summarizer 290 may retrieve user interests and preferences from theproactive inference layer 280. The summarizer 290 may then retrieveentities associated with the user interests and preferences from theentity resolution module 240. The summarizer 290 may further retrieve auser profile from the user context engine 225. Based on the informationfrom the proactive inference layer 280, the entity resolution module240, and the user context engine 225, the summarizer 290 may generatepersonalized and context-aware summaries for the user. In particularembodiments, the summarizer 290 may send the summaries to the CUcomposer 270. The CU composer 270 may process the summaries and send theprocessing results to the assistant xbot 215. The assistant xbot 215 maythen send the processed summaries to the client system 130 via themessaging platform 205 or the TTS module 275. More information onsummarization may be found in U.S. patent application Ser. No.15/967290, filed 30 Apr. 2018, which is incorporated by reference.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system 140. In particular embodiments, theassistant xbot 215 may access a request manager 305 upon receiving theuser request. The request manager 305 may comprise a context extractor306 and a conversational understanding object generator (CU objectgenerator) 307. The context extractor 306 may extract contextualinformation associated with the user request. The context extractor 306may also update contextual information based on the assistantapplication 136 executing on the client system 130. As an example andnot by way of limitation, the update of contextual information maycomprise content items are displayed on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise whether an alarm is set on the client system130. As another example and not by way of limitation, the update ofcontextual information may comprise whether a song is playing on theclient system 130. The CU object generator 307 may generate particularcontent objects relevant to the user request. The content objects maycomprise dialog-session data and features associated with the userrequest, which may be shared with all the modules of the assistantsystem 140. In particular embodiments, the request manager 305 may storethe contextual information and the generated content objects in datastore 310 which is a particular data store implemented in the assistantsystem 140.

In particular embodiments, the request manger 305 may send the generatedcontent objects to the NLU module 220. The NLU module 220 may perform aplurality of steps to process the content objects. At step 221, the NLUmodule 220 may generate a whitelist for the content objects. Inparticular embodiments, the whitelist may comprise interpretation datamatching the user request. At step 222, the NLU module 220 may perform afeaturization based on the whitelist. At step 223, the NLU module 220may perform domain classification/selection on user request based on thefeatures resulted from the featurization to classify the user requestinto predefined domains. The domain classification/selection results maybe further processed based on two related procedures. At step 224 a, theNLU module 220 may process the domain classification/selection resultusing an intent classifier. The intent classifier may determine theuser's intent associated with the user request. In particularembodiments, there may be one intent classifier for each domain todetermine the most possible intents in a given domain. As an example andnot by way of limitation, the intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined intent. At step 224 b, the NLUmodule may process the domain classification/selection result using ameta-intent classifier. The meta-intent classifier may determinecategories that describe the user's intent. In particular embodiments,intents that are common to multiple domains may be processed by themeta-intent classifier. As an example and not by way of limitation, themeta-intent classifier may be based on a machine-learning model that maytake the domain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedmeta-intent. At step 225 a, the NLU module 220 may use a slot tagger toannotate one or more slots associated with the user request. Inparticular embodiments, the slot tagger may annotate the one or moreslots for the n-grams of the user request. At step 225 b, the NLU module220 may use a meta slot tagger to annotate one or more slots for theclassification result from the meta-intent classifier. In particularembodiments, the meta slot tagger may tag generic slots such asreferences to items (e.g., the first), the type of slot, the value ofthe slot, etc. As an example and not by way of limitation, a userrequest may comprise “change 500 dollars in my account to Japanese yen.”The intent classifier may take the user request as input and formulateit into a vector. The intent classifier may then calculate probabilitiesof the user request being associated with different predefined intentsbased on a vector comparison between the vector representing the userrequest and the vectors representing different predefined intents. In asimilar manner, the slot tagger may take the user request as input andformulate each word into a vector. The intent classifier may thencalculate probabilities of each word being associated with differentpredefined slots based on a vector comparison between the vectorrepresenting the word and the vectors representing different predefinedslots. The intent of the user may be classified as “changing money”. Theslots of the user request may comprise “500”, “dollars”, “account”, and“Japanese yen”. The meta-intent of the user may be classified as“financial service”. The meta slot may comprise “finance”.

In particular embodiments, the NLU module 220 may improve the domainclassification/selection of the content objects by extracting semanticinformation from the semantic information aggregator 230. In particularembodiments, the semantic information aggregator 230 may aggregatesemantic information in the following way. The semantic informationaggregator 230 may first retrieve information from the user contextengine 225. In particular embodiments, the user context engine 225 maycomprise offline aggregators 226 and an online inference service 227.The offline aggregators 226 may process a plurality of data associatedwith the user that are collected from a prior time window. As an exampleand not by way of limitation, the data may include news feedposts/comments, interactions with news feed posts/comments, Instagramposts/comments, search history, etc. that are collected from a prior90-day window. The processing result may be stored in the user contextengine 225 as part of the user profile. The online inference service 227may analyze the conversational data associated with the user that arereceived by the assistant system 140 at a current time. The analysisresult may be stored in the user context engine 225 also as part of theuser profile. In particular embodiments, both the offline aggregators226 and online inference service 227 may extract personalizationfeatures from the plurality of data. The extracted personalizationfeatures may be used by other modules of the assistant system 140 tobetter understand user input. In particular embodiments, the semanticinformation aggregator 230 may then process the retrieved information,i.e., a user profile, from the user context engine 225 in the followingsteps. At step 231, the semantic information aggregator 230 may processthe retrieved information from the user context engine 225 based onnatural-language processing (NLP). In particular embodiments, thesemantic information aggregator 230 may tokenize text by textnormalization, extract syntax features from text, and extract semanticfeatures from text based on NLP. The semantic information aggregator 230may additionally extract features from contextual information, which isaccessed from dialog history between a user and the assistant system140. The semantic information aggregator 230 may further conduct globalword embedding, domain-specific embedding, and/or dynamic embeddingbased on the contextual information. At step 232, the processing resultmay be annotated with entities by an entity tagger. Based on theannotations, the semantic information aggregator 230 may generatedictionaries for the retrieved information at step 233. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. At step 234, the semanticinformation aggregator 230 may rank the entities tagged by the entitytagger. In particular embodiments, the semantic information aggregator230 may communicate with different graphs 330 including social graph,knowledge graph, and concept graph to extract ontology data that isrelevant to the retrieved information from the user context engine 225.In particular embodiments, the semantic information aggregator 230 mayaggregate the user profile, the ranked entities, and the informationfrom the graphs 330. The semantic information aggregator 230 may thensend the aggregated information to the NLU module 220 to facilitate thedomain classification/selection.

In particular embodiments, the output of the NLU module 220 may be sentto a co-reference module 315 to interpret references of the contentobjects associated with the user request. In particular embodiments, theco-reference module 315 may be used to identify an item to which theuser request refers. The co-reference module 315 may comprise referencecreation 316 and reference resolution 317. In particular embodiments,the reference creation 316 may create references for entities determinedby the NLU module 220. The reference resolution 317 may resolve thesereferences accurately. As an example and not by way of limitation, auser request may comprise “find me the nearest supermarket and direct methere”. The co-reference module 315 may interpret “there” as “thenearest supermarket”. In particular embodiments, the co-reference module315 may access the user context engine 225 and the dialog engine 235when necessary to interpret references with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution module 240 to resolve relevantentities. The entity resolution module 240 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution module 240 may comprise domain entity resolution 241 andgeneric entity resolution 242. The domain entity resolution 241 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 330. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 242 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 330. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a Tesla car, the generic entity resolution 242 may resolve a Teslacar as vehicle and the domain entity resolution 241 may resolve theTesla car as electric car.

In particular embodiments, the output of the entity resolution module240 may be sent to the dialog engine 235 to forward the flow of theconversation with the user. The dialog engine 235 may comprise dialogintent resolution 236 and dialog state update/ranker 237. In particularembodiments, the dialog intent resolution 236 may resolve the userintent associated with the current dialog session based on dialoghistory between the user and the assistant system 140. The dialog intentresolution 236 may map intents determined by the NLU module 220 todifferent dialog intents. The dialog intent resolution 236 may furtherrank dialog intents based on signals from the NLU module 220, the entityresolution module 240, and dialog history between the user and theassistant system 140. In particular embodiments, the dialog stateupdate/ranker 237 may update/rank the dialog state of the current dialogsession. As an example and not by way of limitation, the dialog stateupdate/ranker 237 may update the dialog state as “completed” if thedialog session is over. As another example and not by way of limitation,the dialog state update/ranker 237 may rank the dialog state based on apriority associated with it.

In particular embodiments, the dialog engine 235 may communicate with atask completion module 335 about the dialog intent and associatedcontent objects. In particular embodiments, the task completion module335 may rank different dialog hypotheses for different dialog intents.The task completion module 335 may comprise an action selectioncomponent 336. In particular embodiments, the dialog engine 235 mayadditionally check against dialog policies 320 regarding the dialogstate. In particular embodiments, a dialog policy 320 may comprise adata structure that describes an execution plan of an action by an agent340. An agent 340 may select among registered content providers tocomplete the action. The data structure may be constructed by the dialogengine 235 based on an intent and one or more slots associated with theintent. A dialog policy 320 may further comprise multiple goals relatedto each other through logical operators. In particular embodiments, agoal may be an outcome of a portion of the dialog policy and it may beconstructed by the dialog engine 235. A goal may be represented by anidentifier (e.g., string) with one or more named arguments, whichparameterize the goal. As an example and not by way of limitation, agoal with its associated goal argument may be represented as{confirm_artist, args: {artist: “Madonna”}}. In particular embodiments,a dialog policy may be based on a tree-structured representation, inwhich goals are mapped to leaves of the tree. In particular embodiments,the dialog engine 235 may execute a dialog policy 320 to determine thenext action to carry out. The dialog policies 320 may comprise genericpolicy 321 and domain specific policies 322, both of which may guide howto select the next system action based on the dialog state. Inparticular embodiments, the task completion module 335 may communicatewith dialog policies 320 to obtain the guidance of the next systemaction. In particular embodiments, the action selection component 336may therefore select an action based on the dialog intent, theassociated content objects, and the guidance from dialog policies 320.

In particular embodiments, the output of the task completion module 335may be sent to the CU composer 270. In alternative embodiments, theselected action may require one or more agents 340 to be involved. As aresult, the task completion module 335 may inform the agents 340 aboutthe selected action. Meanwhile, the dialog engine 235 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' response. Inparticular embodiments, the CU composer 270 may generate a communicationcontent for the user using the NLG 271 based on the output of the taskcompletion module 335. In particular embodiments, the NLG 271 may usedifferent language models and/or language templates to generate naturallanguage outputs. The generation of natural language outputs may beapplication specific. The generation of natural language outputs may bealso personalized for each user. The CU composer 270 may also determinea modality of the generated communication content using the UI payloadgenerator 272. Since the generated communication content may beconsidered as a response to the user request, the CU composer 270 mayadditionally rank the generated communication content using a responseranker 273. As an example and not by way of limitation, the ranking mayindicate the priority of the response.

In particular embodiments, the output of the CU composer 270 may be sentto a response manager 325. The response manager 325 may performdifferent tasks including storing/updating the dialog state 326retrieved from data store 310 and generating responses 327. Inparticular embodiments, the output of CU composer 270 may comprise oneor more of natural- language strings, speech, or actions withparameters. As a result, the response manager 325 may determine whattasks to perform based on the output of CU composer 270. In particularembodiments, the generated response and the communication content may besent to the assistant xbot 215. In alternative embodiments, the outputof the CU composer 270 may be additionally sent to the TTS module 275 ifthe determined modality of the communication content is audio. Thespeech generated by the TTS module 275 and the response generated by theresponse manager 325 may be then sent to the assistant xbot 215.

Gesture-Input Workflow

FIG. 4 illustrates an example workflow of processing a user inputcomprising a gesture-input. A gesture-input may be an input which isbased on a movement of part of the body, especially a hand or the head,to express an idea or meaning. The gesture-inputs may be in the form ofimage information, video information, motion information, or anycombination thereof In particular embodiments, the assistant system 140may receive a user input 405 by a user from a client system 130. Theassistant system 140 may send the user input 405 to anintent-understanding module 410. The intent-understanding module 410 maycomprise a natural-language understanding (NLU) module 220 and agesture-classification model 415. The gesture-classification model 415may be a machine-learning model trained offline to recognize differentcategories of gestures performed by users. As an example and not by wayof limitation, a gesture classification model 415 may be based on one ormore of convolutional neural networks, tensor flow, or hidden Markovmodels. Based on the user input 405, the intent-understanding module 410may use different components to determine an intent 420 associated withthe user input 405. If the user input 405 comprises a text-input or aspeech-input, the intent-understanding module 410 may use the NLU module220 to determine the intent 420. If the user input 405 comprises agesture-input, the intent-understanding module 410 may use thegesture-classification model 415 to determine the intent 420. Thedetermined intent 420 may be associated with a confidence scoreindicating how confident the assistant system 140 is in determining theuser's intent 420. In particular embodiments, the confidence score maybe determined based on how closely the user's text-input or speech-inputmatches a known input for a given intent 420. As an example and not byway of limitation, the closeness may be based on string similaritybetween the text-input and the known input (text). The assistant system140 may then send the intent 420 with its confidence score to the dialogengine 235. If the confidence score is above a threshold score, thedialog engine 235 may determine one or more tasks 425 corresponding tothe intent 420 and send them to either 1st-party agents 250 or 3rd-partyagents 255 for executing the tasks 425. The execution results 430 may besent to the CU composer 270. If the confidence score is below athreshold score, the dialog engine 235 may determine one or moresuggested inputs 435 for the user, which may help the assistant system140 determine the user's intent with a higher confidence score. Thedialog engine 235 may send the suggested inputs 435 to the CU composer270. Based on the execution results 430 or the suggested inputs 435, theCU composer 270 may generate a response 440. The response 440 may be indifferent modalities comprising one or more of a text, an image, avideo, or an animation of a gesture. The CU composer 270 may furthersend the response 440 to the client system 130. Although this disclosuredescribes processing a user input via particular systems in particularmanners, this disclosure contemplates describes processing any suitableuser input via any suitable system in any suitable manner.

Auto-Completion for Multi-modal User Input in Assistant Systems

In particular embodiments, the assistant system 140 may receive aninitial input in a first modality executed by a user as an interactionwith an assistant-based client system 130, e.g., a gesture-input to avirtual reality (VR) headset or augment reality (AR) smart glasses,determine candidate continuation-inputs in an auto-completion mannerbased on the initial input, and recommend these candidatecontinuation-inputs to the user in one or more second modalities (e.g.,text), for which the user may select one of them to trigger theexecution of a particular task 425. The initial input andcontinuation-inputs may be based on any suitable modality includingtext, speech, image, video, motion, orientation, etc. In addition, themodality of the initial input and the modalities of thecontinuation-inputs may be different. As an example and not by way oflimitation, a user visiting Paris who wears AR glasses may be looking atEiffel Tower. Based on the gaze input, the assistant system 140 maysuggest a gesture displayed on the virtual screen for taking a pictureor suggest the user to speak “take a picture.” The user may perform thesuggested gesture or speak the sentence to allow the assistant system140 to execute the task of taking a picture of Eiffel Tower. As aresult, the assistant system 140 may have the ability to process a userinput 405 in one modality and generate suggested inputs in one or moreother modalities, which may be referred as auto-completion formulti-modal user input. Although this disclosure describes generatingparticular auto-completion for particular multi-modal user inputs viaparticular systems in particular manners, this disclosure contemplatesgenerating any suitable auto-completion for any suitable multi-modaluser input via any suitable system in any suitable manner.

In particular embodiments, the assistant system 140 may receive, from aclient system 130 associated with a first user, an initial input fromthe first user. The initial input may be in a first modality. Inparticular embodiments, the assistant system 140 may determine, by anintent-understanding module, one or more intents 420 corresponding tothe initial input. The assistant system 140 may then generate, based onthe one or more intents 420, one or more candidate continuation-inputs.The one or more candidate continuation-inputs may be in one or morecandidate modalities, respectively. The candidate modalities may bedifferent from the first modality. In particular embodiments, theassistant system 140 may further send, to the client system 130,instructions for presenting one or more suggested inputs correspondingto one or more of the candidate continuation-inputs.

In particular embodiments, the first modality may comprise one of audio,text, image, video, motion, or orientation. As an example and not by wayof limitation, the first modality may comprise motion, andcorrespondingly the initial input may comprise a gesture. As anotherexample and not by way of limitation, the first modality may compriseorientation, and correspondingly the initial input may comprise a gazeon an object. In particular embodiments, the first modality and thecandidate modalities for the candidate continuation-inputs may bedifferent. As an example and not by way of limitation, a user may bewearing AR glasses and his/her initial input may be a framing gesture bytwo hands for locating an object in front of him/her. The assistantsystem 140 may suggest the user to say “click” as a candidatecontinuation-input which is displayed as text on the screen of the ARglasses. Once the user says “click” the assistant system 140 may furtherexecute the task of taking a picture of the object in the frame. As aresult, the assistant system 140 may have technical advantage ofenriching user experience with the assistant system 140 as a user mayinteract with the assistant system 140 with inputs in variousmodalities. Although this disclosure describes particular modalities inparticular manners, this disclosure contemplates any suitable modalityin any suitable manner.

In particular embodiments, the assistant system 140 may determine thecandidate continuation-inputs based on the intent of the initial inputand the potential entities relevant to the intent 420. The assistantsystem 140 may first identify one or more entities associated with theone or more intents 420. The assistant system 140 may then generate theone or more candidate continuation-inputs further based the one or moreentities. As an example and not by way of limitation, a user may performa calling gesture as an initial input. The assistant system 140 maydetermine a calling intent and possible entities (e.g., contacts)associated with the calling intent. The entities may be suggested to theuser as text and the assistant system 140 may additionally ask the userto say the name of the entity that the user intended to call.Determining the candidate continuation-inputs based on the intent 420 ofthe initial input and the potential entities relevant to the intent 420may be an effective solution for addressing the technical challenge ofdetermining candidate continuation-inputs based on the initial input, asthe intent 420 and the potential entities may be necessary to determinewhat continuation-input a user may use to interact with the assistantsystem 140 to execute tasks that based on them. Although this disclosuredescribes determining particular inputs in particular manners, thisdisclosure contemplates determining any suitable input in any suitablemanner.

In particular embodiments, the assistant system 140 may determine thatthe first user needs one or more suggested inputs. The assistant system140 may determine whether to suggest candidate continuation-inputs basedon different factors. In particular embodiments, determining that thefirst user needs one or more suggested inputs may be based on a wake-upinput from the first user. The wake-up input may comprise one or more ofa voice utterance, a character string, an image, a video clip, agesture, or a gaze. As an example and not by way of limitation, thewake-up input may be a voice command saying “assistant!” or a gesturesuch as snapping fingers. Once the assistant system 140 receives awake-up input, it may initiate the process for suggesting candidatecontinuation-inputs. The assistant system 140 may be constantly in anactive status for suggesting candidate continuation-inputs. In addition,whether to suggest continuation-inputs may be based on user attention,i.e., gaze information, detected by the assistant system 140. Inparticular embodiments, the initial input may comprise a gaze on anobject and determining that the first user needs one or more suggestedinputs may be further based on the gaze on the object. On the otherhand, generating the one or more candidate continuation-inputs may befurther based on the object. Determining the candidatecontinuation-inputs based on the object associated with the initialinput may be an effective solution for addressing the technicalchallenge of determining candidate continuation-inputs based on theinitial input, as the object may be necessary to determine whatcontinuation-input a user may use to interact with the assistant systemto execute tasks that based on it. As an example and not by way oflimitation, if a user who wears AR glasses is staring at a pair of shoesat a store, the assistant system 140 may determine that the user needssuggested inputs and generate suggested inputs such as “taking apicture” and “checking the reviews,” etc. The suggested inputs may bebased on any suitable modality. For example, the suggested inputs may bepresented as gestures displayed in the screen of the AR glasses, whichthe user can follow to perform. As another example, the suggested inputsmay be audio-based, which the user can listen to. As another example andnot by way of limitation, a user is having a video call on the ARglasses and then the user turned around and looked at a TV for athreshold amount of time. The assistant system 140 may then determinethat the user needs suggested inputs. The assistant system 140 maypresent a suggested input that instructs the user to point to the TV ifthe user wants the video call to be mirrored to the TV. In particularembodiments, determining that the first user needs one or more suggestedinputs may be further based on contextual information associated withthe initial input. In particular embodiments, contextual information maycomprise one or more of temporal information, location information,presence information, or social information. As an example and not byway of limitation, the user may input only a few letters and thenstopped inputting text for over a certain amount of time. The assistantsystem 140 may then determine that the user needs suggested inputs asthe user may be having difficulty inputting the intended text. Asanother example and not by way of limitation, the user may be at aself-service restaurant and send an image of a dish to the assistantsystem 140. The assistant system 140 may then determine that user needssuggested inputs such as the name of the dish or a request to order thatdish. In particular embodiments, determining that the first user needsone or more suggested inputs may be further based on the one or moreintents 420. As an example and not by way of limitation, a user'sinitial input may indicate that the user wants to buy a movie ticket butthe user did not provide any additional information. The assistantsystem 140 may then determine that the user needs suggested inputs suchas names of recent popular movies. Although this disclosure describesdetermining whether to suggest particular inputs in particular manners,this disclosure contemplates determining whether to suggest any suitableinput in any suitable manner.

In particular embodiments, the assistant system 140 may further receive,from the client system 130, a user-selected input from the first user.The user-selected input may comprise one of the suggested inputs. Theassistant system 140 may then execute one or more tasks 425 based on theuser-selected input. In particular embodiments, the assistant system 140may suggest candidate continuation-inputs for more than once. In otherwords, the assistant system 140 may guide the user through a sequence ofcandidate continuation-inputs, in which each of the candidatecontinuation-inputs may be mapped to a specific intent 420. To be morespecific, the assistant system 140 may receive, from the client system130, a first user-selected input from the first user. The firstuser-selected input may comprise one of the suggested inputs and thefirst user-selected input may be associated with a first intent 420. Theassistant system 140 may then generate, based on the first user-selectedinput, one or more additional candidate continuation-inputs. Each of theone or more additional candidate continuation-inputs may be associatedwith the first intent 420. The assistant system 140 may then send, tothe client system 130, instructions for presenting one or moreadditional suggested inputs corresponding to one or more of theadditional candidate continuation-inputs. The assistant system 140 maythen receive, from the client system 130, a second user-selected inputfrom the first user. The second user-selected input may comprise one ofthe additional suggested inputs. The assistant system 140 may furtherexecute one or more tasks 425 based on the second user-selected input.The one or more tasks 425 may correspond to the first intent 420. Inparticular embodiments, the assistant system 140 may continue theaforementioned process until one or more tasks 425 that require a wholesequence of inputs are executed. As an example and not by way oflimitation, a user wearing AR glasses may want to set a reminder byperforming a “reminder” gesture. The assistant system 140 may recognizesuch gesture and generate suggestions of what events the reminder isfor. The assistant system 140 may present three suggested events intext, e.g., “mow the lawn”, “feed the dog”, and “buy a gift for Mom”.The user may tap “feeding the dog”. The assistant system 140 may furthergenerate suggested time (e.g., 10 am, 3 pm, and 7 pm) and ask the userto select the time either by tapping or speaking. After the user selectsthe time (e.g., by speaking “7 pm”), the assistant system 140 may setthe reminder accordingly. As a result, the assistant system 140 may havea technical advantage of increasing the degree of users engaging withthe assistant system 140 by guiding users with sequences of suggestedinputs to explore various tasks that the users may request assistance.Although this disclosure describes generating particular sequences ofinputs in particular manners, this disclosure contemplates generatingany suitable sequence of input in any suitable manner.

Auto-Completion for Gesture-Input in Assistant Systems

In particular embodiments, the assistant system 140 may receive anincomplete gesture executed by a user as an interaction with anassistant-based client system 130, e.g., a VR headset or AR smartglasses, determine candidate gestures in an auto-completion manner basedon the incomplete gesture, recommend these candidate gestures to theuser, for which the user may select one of them to trigger the executionof a particular task 425. As an example and not by way of limitation, auser wearing AR glasses may not know what gesture to perform to triggera particular function of the assistant system 140. The user may startmoving his/her hand but pause in the air. Accordingly, the assistantsystem 140 may analyze the incomplete gesture of the user and determinepossible candidate gestures. The assistant system 140 may display thesecandidate gestures visually via the AR glasses to the user. After theuser selects one of them, the assistant system 140 may further execute atask 425 corresponding to that gesture. In summary, the assistant system140 may use gesture recognition techniques on incomplete gestures todetermine the user-intended gestures and suggest the determined gesturesas guidance to the user, which may be referred as auto-completion forgesture-input. Although this disclosure describes generating particularauto-completion for particular gesture-inputs via particular systems inparticular manners, this disclosure contemplates generating any suitableauto-completion for any suitable gesture-input via any suitable systemin any suitable manner.

In particular embodiments, the assistant system 140 may receive, from aclient system associated with a first user, a user input 405 from thefirst user. The user input 405 may comprise an incomplete gestureperformed by the first user. In particular embodiments, the assistantsystem 140 may calculate, by an intent-understanding module, one or moreconfidences scores for one or more intents 420 corresponding to theincomplete gesture. The assistant system 140 may then determine that thecalculated confidence scores associated with each of the intents 420 arebelow a threshold score. In particular embodiments, the assistant system140 may select, based on a personalized gesture-recognition model, oneor more candidate gestures from a plurality of pre-defined gesturesresponsive to determining that the calculated confidence scores for eachof the intents 420 are below the threshold score. Each of the candidategestures may be associated with a confidence score representing alikelihood the first user intended to input the respective candidategesture. In particular embodiments, the assistant system 140 may furthersend, to the client system 130, instructions for presenting one or moresuggested inputs corresponding to one or more of the candidate gestures.

In particular embodiments, the assistant system 140 may recognize anincomplete gesture of a user input 405. An incomplete gesture maycomprise the onset of a gesture, which may indicate that the user hasnot completed the whole gesture yet. As an example and not by way oflimitation, an open hand pausing in the air may be an incomplete gesturefor the waving gesture. In particular embodiments, the assistant system140 may suggest a list of pre-defined gestures, one of which may be thegesture that the user intended to perform. As an example and not by wayof limitation, each pre-defined gesture may comprise one or more ofpointing, poking, tapping, waving, or swiping. As a result, theassistant system 140 may have a technical advantage of improving userexperience with the assistant system 140 as the assistant system 140 mayunderstand a user's intent 420 based on an incomplete gesture and teacha user to complete the incomplete gesture to interact with the assistantsystem 140. Although this disclosure describes particular gestures inparticular manners, this disclosure contemplates any suitable gesture inany suitable manner.

In particular embodiments, the assistant system 140 may determine auser's intended gesture based on similarity evaluation with respect to alist of pre-defined gestures. The assistant system 140 may calculate,for each of the one or more candidate gestures, a similarity level ofthe candidate gesture with respect to the incomplete gesture. As anexample and not by way of limitation, if a user's hand is in aparticular position, the assistant system 140 may determine how similarthis incomplete gesture is to the pre-defined gestures. Afterdetermining which pre-defined gestures this uncompleted gesture is mostsimilar to, the assistant system 140 may select them as candidategestures. In particular embodiments, the assistant system 140 maydetermine whether to present candidate gestures to a user based onsimilarity levels. As an example and not by way of limitation, if thesimilarity levels for all the candidate gestures with respect to theincomplete gesture are below a pre-defined threshold level, theassistant system 140 may present them to the user for selection. Asanother example and not by way of limitation, if two or more candidategestures have similar similarity levels which are greater than thepre-defined threshold level, the assistant system 140 may still presentthem to the user for disambiguation. In particular embodiments,selecting the one or more candidate gestures may be further based on theone or more intents 420. As an example and not by way of limitation, theintents 420 may comprise locating an object or interacting with anotheruser on social media. Correspondingly, the candidate gestures maycomprise pointing or poking. In particular embodiments, the assistantsystem 140 may further suggest inputs which may guide the user tocomplete the candidate gestures. Although this disclosure describesdetermining particular candidate gestures in particular manners, thisdisclosure contemplates determining any suitable candidate gesture inany suitable manner.

In particular embodiments, the assistant system 140 may determine thesimilarity level in different ways. In particular embodiments, thesimilarly level of each candidate gesture with respect to the incompletegesture may be based on a trajectory of the incomplete gesture withrespect to the client system. As an example and not by way oflimitation, if a user moved his/her hand from left to right and paused,most likely the assistant system 140 may determine some gestures thatcontinue the motion in a relatively similar trajectory, e.g., swiping.But if the user moved his/her hand from the bottom to the top, thetrajectory is different, for which the assistant system 140 maydetermine different gestures, e.g., pointing. In particular embodiments,the similarly level of each candidate gesture with respect to theincomplete gesture may be based on an orientation of the incompletegesture with respect to the client system 130. As an example and not byway of limitation, if the orientation is parallel to the client system130, the gestures of waving or swiping may have higher similarity levelwith respect to the incomplete gesture. As another example and not byway of limitation, if the orientation is perpendicular to the clientsystem 130, the gestures of pointing or poking may have highersimilarity level with respect to the incomplete gesture. In particularembodiments, the similarly level of each candidate gesture with respectto the incomplete gesture may be based on an object associated with theincomplete gesture. As an example and not by way of limitation, thedetermined gestures corresponding to a phone may be different from thosecorresponding to a TV. In particular embodiments, the similarly level ofeach candidate gesture with respect to the incomplete gesture may bebased on contextual information associated with the incomplete gesture.As an example and not by way of limitation, if the contextualinformation indicates that the user is browsing another user's socialmedia profile, the candidate gesture may be more likely poking ratherthan pointing. In particular embodiments, the similarly level of eachcandidate gesture with respect to the incomplete gesture may be based ona position of the incomplete gesture with respect to the client system130. As an example and not by way of limitation, if the incompletegesture is very close to the client system 130, the candidate gesturemay be more likely tapping. One technical challenge includes determiningsimilarity levels of candidate gestures with respect to the incompletegesture. Determining the similarity levels based on different factorsincluding trajectory, orientation, objects, contextual information, andposition associated with the incomplete gesture may be an effectivesolution for addressing the technical challenge of determiningsimilarity levels of candidate gestures with respect to the incompletegesture, as such information may provide different informative cues forthe assistant system 140 to calculate similarity levels. Although thisdisclosure describes determining particular similarity levels inparticular manners, this disclosure contemplates determining anysuitable similarity level in any suitable manner.

In particular embodiments, the assistant system 140 may determinewhether the first user needs suggestions to complete the incompletegesture based on the calculated confidence scores for the intents 420corresponding to the incomplete gesture. If the confidence scores arebelow a threshold score, it may indicate that the assistant system 140cannot determine the user's intent 420. Such difficulty in determiningthe user's intent 420 may be caused by the fact that the incompletegesture is uninformative, which further indicates that the user does notknow how to perform the intended gesture and may need suggestions. Inparticular embodiments, the threshold score may be based on a wake-upgesture performed by the first user. The wake-up gesture may indicatethat the user clearly needs suggestions. As a result, the assistantsystem 140 may set a high threshold score, which results in thecalculated confidence scores being below the threshold score. Theassistant system 140 may then generate candidate gestures accordingly.In summary, once a user has made a wake-up gesture, the assistant system140 may initiate the process for gesture suggestion. The assistantsystem may be constantly in an active status for gesture suggestion. Inparticular embodiments, calculating the one or more confidence scoresfor the one or more intents 420 corresponding to the incomplete gesturemay be based on temporal information associated with the incompletegesture. The temporal information may comprise a pause in the user input405. As an example and not by way of limitation, the calculatedconfidence scores may be low when there is a pause in the user input405. Accordingly, the assistant system 140 may determine that the userneeds suggestions to complete the incomplete gesture. In particularembodiments, calculating the one or more confidence scores for the oneor more intents 420 corresponding to the incomplete gesture may be basedon a velocity associated with the incomplete gesture. As an example andnot by way of limitation, the assistant system 140 may monitor how fasta user's movement is. If the user is moving fairly confident and fastthe user probably already knew the gesture that he/she wants to perform,for which the assistant system 140 may calculate high confidence scoresand not suggest any gestures. If the assistant system 140 detects thatthe user is hesitant of performing a gesture (e.g., moving slow), theassistant system 140 may infer that the user is trying to figure outwhat gestures he/she can do, calculate low confidence scores, andsuggest gestures accordingly. In particular embodiments, determining thefirst user needs suggestions to complete the incomplete gesture may befurther based on patterns of the incomplete gesture. As an example andnot by way of limitation, if a user is repeating the same incompletegesture, the assistant system 140 may determine a suggested gesture tocomplete the user's intended gesture. Determining whether to suggestcandidate gestures based on different factors including confidencescores of intents, wake-up gestures, temporal information of theincomplete gesture, velocity of the incomplete gesture, and patterns ofthe incomplete gesture may be an effective solution for addressing thetechnical challenge of determining that a user needs suggested inputsfor gestures, as such information may reveal a user's requirement forassistance from different perspectives. Although this disclosuredescribes determining whether a user needs particular suggested gesturesin particular manners, this disclosure contemplates determining whethera user needs any suitable suggested gestures in any suitable manner.

In particular embodiments, the assistant system 140 may receive, fromthe client system 130, a user-selected input from the first user. Theuser-selected input may comprise one of the suggested inputs. Theassistant system 140 may then execute one or more tasks based on theuser-selected input. In particular embodiments, the assistant system 140may suggest candidate gestures for more than once. In other words, theassistant system 140 may guide the user through a sequence of candidategestures, in which each of the candidate gestures may be mapped to aspecific intent 420. To be more specific, the assistant system 140 mayreceive, from the client system 130, a first user-selected input fromthe first user. The first user-selected input may comprise one of thesuggested inputs and the first user-selected input may be associatedwith a first intent 420. The assistant system 140 may then generate,based on the first user-selected input, one or more additional candidategestures. Each of the one or more additional candidate gestures may beassociated with the first intent 420. The assistant system 140 may thensend, to the client system 130, instructions for presenting one or moreadditional suggested inputs corresponding to one or more of theadditional candidate gestures. The assistant system 140 may thenreceive, from the client system 130, a second user-selected input fromthe first user. The second user-selected input may comprise one of theadditional suggested inputs. The assistant system 140 may furtherexecute one or more tasks 425 based on the second user-selected input.The one or more tasks 425 may correspond to the first intent 420. Inparticular embodiments, the assistant system 140 may continue theaforementioned process until one or more tasks 425 that require a wholesequence of gestures are executed. As an example and not by way oflimitation, a user wearing AR glasses may want to make a call and haveperformed an incomplete gesture. The assistant system 140 may suggest afew candidate gestures including the “call” gesture. The user may thencomplete the “call” gesture based on the suggestion. The assistantsystem 140 may then display three different people (e.g., names orphotos) on the virtual screen of the VR glasses and suggest additionalgestures (e.g., pointing or tapping) to the user. Accordingly, the usermay perform one of the additional suggested gestures on someone he/shewants to call. Finally, the assistant system 140 may execute the task425 of calling the intended person. As another example and not by way oflimitation, a user may want to take a picture of a friend and haveperformed an incomplete gesture. The assistant system 140 may suggest afew candidate gestures including the “taking-pictures” gesture to theuser. The user may then complete the “taking-pictures” gesture based onthe suggestion. After the picture is taken, the assistant system 140 mayfurther suggest some additional gestures that the user could perform onthe picture. For example, one gesture may be a gesture for sending thispicture to the friend. The user may thus perform such gesture to finisha sequence of gestures and the assistant system 140 may send the pictureto the friend subsequently. As a result, the assistant system 140 mayhave a technical advantage of increasing the degree of users engagingwith the assistant system 140 by guiding users with sequences ofsuggested gestures to explore various tasks that the users may requestassistance. Although this disclosure describes suggesting particularsequence of gestures in particular manners, this disclosure contemplatessuggesting any suitable sequence of gestures in any suitable manner.

In particular embodiments, determining candidate gestures responsive toa user's incomplete gesture may be applied to user education inassistant-based AR/VR systems. As an example and not by way oflimitation, when a user initially puts on the AR glasses he may not knowwhat gestures he can use to interact with the assistant system 140. Theuser may raise his/her hand wondering what to do. In this case, theassistant system 140 may analyze the user's current incomplete gestureand determine what gestures may be similar gestures that the user canperform. Accordingly, the assistant system 140 may suggest thedetermined gestures by displaying them virtually on the screen of the ARglasses. In particular embodiments, determining candidate gesturesresponsive to a user's incomplete gesture may be suitable when onlygestures are available for interacting with the assistant system 140. Asan example and not by way of limitation, a user cannot speak and doesnot have a keyboard for texting. In this case, if the user startshis/her interaction with the assistant system 140 with an incompletegesture, the assistant system 140 may suggest the user to perform one ormore gestures to complete a particular task 425. Although thisdisclosure describes particular applications of the embodimentsdisclosed herein in particular manners, this disclosure contemplates anysuitable application of the embodiments disclosed herein in any suitablemanner.

FIG. 5 illustrates an example scenario of auto-completion formulti-modal user input 405. As displayed in FIG. 5, a user 500 may bewearing a pair of AR glasses 505. The AR glasses 505 may function as aclient system 130 for the user 500 to interact with the assistant system140. The user 500 may provide an initial input, which may be a “call”gesture 510. The assistant system 140 may determine the user's intent420 to call a contact corresponding to the “call” gesture 510. Theassistant system 140 may identify two contacts that the user 500 maywant to call, e.g., his Mom and his wife Jane. Based on the identifiedcontacts, the assistant system 140 may generate two candidatecontinuation-inputs including selecting a contact by voice and selectinga contact by gesture. The assistant system 140 may further generatesuggested inputs which may be displayed on the screen 515 of the VRglasses 505. In FIG. 5, the suggested inputs are presented as “who wouldyou like to reach? You may say ‘Mom’ or ‘Jane’ or you may tap on thephoto” 520. In addition, a photo of “Mom” 525 and a photo of “Jane” 530may be presented in association with the suggested inputs. Although thisdisclosure describes particular scenarios of auto-completion forparticular multi-modal user input in particular manners, this disclosurecontemplates any suitable scenario of auto-completion for any suitablemulti-modal user input in any suitable manner.

FIG. 6 illustrates an example scenario of auto-completion forgesture-input. As displayed in FIG. 6, a user 500 may be interactingwith the assistant system 140 via a client system 130. The user 500 maywant to perform a gesture to interact with the assistant system 140 butdoes not really know what gestures he could perform. Therefore, the user500 may hold his hand in the air, which may have formed an incompletegesture 605. Based on the incomplete gesture 605, the assistant system140 may determine one or more candidate gestures using a personalizedgesture-recognition model. As an example and not by way of limitation,the one or more candidate gestures may comprise two gestures, i.e.,waving and tapping. The assistant system 140 may further generatesuggested inputs which may be displayed on the screen 610 of the clientsystem 130. In FIG. 6, the suggested inputs are presented as “pleaseperform one of the following gestures to let me know how I can help you”615. In addition, an animation of the waving gesture 620 and ananimation of the tapping gesture 625 may be presented in associationwith the suggested inputs. Although this disclosure describes particularscenarios of auto-completion for particular gesture-input in particularmanners, this disclosure contemplates any suitable scenario ofauto-completion for any suitable gesture-input in any suitable manner.

FIGS. 7A-7B illustrate another example scenario of auto-completion forgesture-input. FIG. 7A illustrates an example scenario of an incompletegesture in AR/VR setting. As displayed in FIG. 7A, a user 500 may bewearing a pair of AR glasses 505. The AR glasses 505 may function as aclient system 130 for the user 500 to interact with the assistant system140. The user 500 may see a dog on the screen 705 of the AR glasses 505.The user 500 may want to perform a gesture to interact with theassistant system 140 but does not really know what gestures he couldperform. Therefore, the user 500 may hold his hands in the air, whichmay have formed an incomplete gesture 710. Based on the incompletegesture 710 and the focus of the user's attention (i.e., the dog), theassistant system 140 may determine one or more candidate gestures usinga personalized gesture-recognition model. The assistant system 140 mayfurther generate suggested inputs which may be displayed on the screen705 of the AR glasses 505. FIG. 7B illustrates an example scenario ofsuggested gesture-input in AR/VR setting. In FIG. 7B, the suggestedinputs are presented as “please perform the displayed gesture to take apicture” 715. In addition, a “taking a picture” gesture 720 (i.e., indotted lines) may be presented in association with the suggested inputs.Although this disclosure describes particular scenarios ofauto-completion for particular gesture-inputs in particular manners,this disclosure contemplates any suitable scenario of auto-completionfor any suitable gesture-input in any suitable manner.

FIG. 8 illustrates an example method 800 for suggesting multi-modal userinput 405 for auto-completion. The method may begin at step 810, wherethe assistant system 140 may receive, from a client system 130associated with a first user, an initial input from the first user,wherein the initial input is in a first modality. At step 820, theassistant system 140 may determine, by an intent-understanding module410, one or more intents 420 corresponding to the initial input. At step830, the assistant system 140 may generate, based on the one or moreintents 420, one or more candidate continuation-inputs, where the one ormore candidate continuation-inputs are in one or more candidatemodalities, respectively, and wherein the candidate modalities aredifferent from the first modality. At step 840, the assistant system 140may send, to the client system 130, instructions for presenting one ormore suggested inputs corresponding to one or more of the candidatecontinuation-inputs. Particular embodiments may repeat one or more stepsof the method of FIG. 8, where appropriate. Although this disclosuredescribes and illustrates particular steps of the method of FIG. 8 asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method of FIG. 8 occurring in any suitable order.Moreover, although this disclosure describes and illustrates an examplemethod for suggesting multi-modal user input for auto-completion,including the particular steps of the method of FIG. 8, this disclosurecontemplates any suitable method for suggesting multi-modal user inputfor auto-completion, including any suitable steps, which may includeall, some, or none of the steps of the method of FIG. 8, whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 8, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 8.

FIG. 9 illustrates an example method 900 for suggesting candidategestures for auto-completion. The method may begin at step 910, wherethe assistant system 140 may receive, from a client system 130associated with a first user, a user input 405 from the first user,wherein the user input 405 comprises an incomplete gesture performed bythe first user. At step 920, the assistant system 140 may calculate, byan intent-understanding module 410, one or more confidence scores forone or more intents 420 corresponding to the incomplete gesture. At step930, the assistant system 140 may determine that the calculatedconfidence scores associated with each of the intents 420 are below athreshold score. At step 940, the assistant system 140 may select, basedon a personalized gesture-recognition model, one or more candidategestures from a plurality of pre-defined gestures responsive todetermining that the calculated confidence scores for each of theintents 420 are below the threshold score, wherein each of the candidategestures is associated with a confidence score representing a likelihoodthe first user intended to input the respective candidate gesture. Atstep 950, the assistant system 140 may send, to the client system 130,instructions for presenting one or more suggested inputs correspondingto one or more of the candidate gestures. Particular embodiments mayrepeat one or more steps of the method of FIG. 9, where appropriate.Although this disclosure describes and illustrates particular steps ofthe method of FIG. 9 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 9 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for suggesting candidate gestures forauto-completion, including the particular steps of the method of FIG. 9,this disclosure contemplates any suitable method for suggestingcandidate gestures for auto-completion, including any suitable steps,which may include all, some, or none of the steps of the method of FIG.9, where appropriate. Furthermore, although this disclosure describesand illustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 9, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 9.

Social Graphs

FIG. 10 illustrates an example social graph 1000. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 1000 in one or more data stores. In particularembodiments, the social graph 1000 may include multiple nodes—which mayinclude multiple user nodes 1002 or multiple concept nodes 1004—andmultiple edges 1006 connecting the nodes. Each node may be associatedwith a unique entity (i.e., user or concept), each of which may have aunique identifier (ID), such as a unique number or username. The examplesocial graph 1000 illustrated in FIG. 10 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, a client system 130, anassistant system 140, or a third-party system 170 may access the socialgraph 1000 and related social-graph information for suitableapplications. The nodes and edges of the social graph 1000 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 1000.

In particular embodiments, a user node 1002 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 1002 correspondingto the user, and store the user node 1002 in one or more data stores.Users and user nodes 1002 described herein may, where appropriate, referto registered users and user nodes 1002 associated with registeredusers. In addition or as an alternative, users and user nodes 1002described herein may, where appropriate, refer to users that have notregistered with the social-networking system 160. In particularembodiments, a user node 1002 may be associated with informationprovided by a user or information gathered by various systems, includingthe social-networking system 160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 1002 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1002 maycorrespond to one or more web interfaces.

In particular embodiments, a concept node 1004 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web- application server); anentity (such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node1004 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 1004 may be associated with one or more dataobjects corresponding to information associated with concept node 1004.In particular embodiments, a concept node 1004 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 1000 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 1004. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 1002 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 1004 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 1004.

In particular embodiments, a concept node 1004 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 1002 corresponding to the user and a conceptnode 1004 corresponding to the third-party web interface or resource andstore edge 1006 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 1000 maybe connected to each other by one or more edges 1006. An edge 1006connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1006 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 1006 connecting the first user's user node 1002 to thesecond user's user node 1002 in the social graph 1000 and store edge1006 as social-graph information in one or more of data stores 164. Inthe example of FIG. 10, the social graph 1000 includes an edge 1006indicating a friend relation between user nodes 1002 of user “A” anduser “B” and an edge indicating a friend relation between user nodes1002 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 1006 with particular attributes connectingparticular user nodes 1002, this disclosure contemplates any suitableedges 1006 with any suitable attributes connecting user nodes 1002. Asan example and not by way of limitation, an edge 1006 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in the social graph 1000 byone or more edges 1006.

In particular embodiments, an edge 1006 between a user node 1002 and aconcept node 1004 may represent a particular action or activityperformed by a user associated with user node 1002 toward a conceptassociated with a concept node 1004. As an example and not by way oflimitation, as illustrated in FIG. 10, a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “watched” a concept,each of which may correspond to an edge type or subtype. Aconcept-profile interface corresponding to a concept node 1004 mayinclude, for example, a selectable “check in” icon (such as, forexample, a clickable “check in” icon) or a selectable “add to favorites”icon. Similarly, after a user clicks these icons, the social-networkingsystem 160 may create a “favorite” edge or a “check in” edge in responseto a user's action corresponding to a respective action. As anotherexample and not by way of limitation, a user (user “C”) may listen to aparticular song (“Imagine”) using a particular application (an onlinemusic application). In this case, the social-networking system 160 maycreate a “listened” edge 1006 and a “used” edge (as illustrated in FIG.10) between user nodes 1002 corresponding to the user and concept nodes1004 corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 1006 (asillustrated in FIG. 10) between concept nodes 1004 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 1006corresponds to an action performed by an external application (theonline music application) on an external audio file (the song“Imagine”). Although this disclosure describes particular edges 1006with particular attributes connecting user nodes 1002 and concept nodes1004, this disclosure contemplates any suitable edges 1006 with anysuitable attributes connecting user nodes 1002 and concept nodes 1004.Moreover, although this disclosure describes edges between a user node1002 and a concept node 1004 representing a single relationship, thisdisclosure contemplates edges between a user node 1002 and a conceptnode 1004 representing one or more relationships. As an example and notby way of limitation, an edge 1006 may represent both that a user likesand has used at a particular concept. Alternatively, another edge 1006may represent each type of relationship (or multiples of a singlerelationship) between a user node 1002 and a concept node 1004 (asillustrated in FIG. 10 between user node 1002 for user “E” and conceptnode 1004 for the online music application).

In particular embodiments, the social-networking system 160 may createan edge 1006 between a user node 1002 and a concept node 1004 in thesocial graph 1000. As an example and not by way of limitation, a userviewing a concept-profile interface (such as, for example, by using aweb browser or a special-purpose application hosted by the user's clientsystem 130) may indicate that he or she likes the concept represented bythe concept node 1004 by clicking or selecting a “Like” icon, which maycause the user's client system 130 to send to the social-networkingsystem 160 a message indicating the user's liking of the conceptassociated with the concept-profile interface. In response to themessage, the social-networking system 160 may create an edge 1006between user node 1002 associated with the user and concept node 1004,as illustrated by “like” edge 1006 between the user and concept node1004. In particular embodiments, the social-networking system 160 maystore an edge 1006 in one or more data stores. In particularembodiments, an edge 1006 may be automatically formed by thesocial-networking system 160 in response to a particular user action. Asan example and not by way of limitation, if a first user uploads apicture, watches a movie, or listens to a song, an edge 1006 may beformed between user node 1002 corresponding to the first user andconcept nodes 1004 corresponding to those concepts. Although thisdisclosure describes forming particular edges 1006 in particularmanners, this disclosure contemplates forming any suitable edges 1006 inany suitable manner.

Vector Spaces and Embeddings

FIG. 11 illustrates an example view of a vector space 1100. Inparticular embodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 1100 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 1100 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 1100 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 1100(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 1110, 1120, and 1130 may be represented as pointsin the vector space 1100, as illustrated in FIG. 11. An n-gram may bemapped to a respective vector representation. As an example and not byway of limitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 1100, respectively, by applying a function

defined by a dictionary, such that

=

(t₁) and

=

(t₂) . As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a model, such as Word2vec, may be used tomap an n-gram to a vector representation in the vector space 1100. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 1100 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 1100 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 1100, respectively, by applying a function such that

, such that

=

(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function

may map objects to vectors by feature extraction, which may start froman initial set of measured data and build derived values (e.g.,features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 1100. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{\rightarrow}{v_{1}} \cdot \overset{\rightarrow}{v_{2}}}{{\overset{\rightarrow}{v_{1}}}{\overset{\rightarrow}{v_{2}}}}.$

As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 1100. As an example and not by way of limitation,vector 1110 and vector 1120 may correspond to objects that are moresimilar to one another than the objects corresponding to vector 1110 andvector 1130, based on the distance between the respective vectors.Although this disclosure describes calculating a similarity metricbetween vectors in a particular manner, this disclosure contemplatescalculating a similarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 12 illustrates an example artificial neural network (“ANN”) 1200.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 1200 may comprise an inputlayer 1210, hidden layers 1220, 1230, 1240, and an output layer 1250.Each layer of the ANN 1200 may comprise one or more nodes, such as anode 1205 or a node 1215. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 1210 may be connected toone of more nodes of the hidden layer 1220. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 12 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 12 depicts a connection between each node of the inputlayer 1210 and each node of the hidden layer 1220, one or more nodes ofthe input layer 1210 may not be connected to one or more nodes of thehidden layer 1220.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 1220 may comprise the output of one or morenodes of the input layer 1210. As another example and not by way oflimitation, the input to each node of the output layer 1250 may comprisethe output of one or more nodes of the hidden layer 1240. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k)(s_(k))=max(0, s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection1225 between the node 1205 and the node 1215 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 1205 is used as an input to the node 1215. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 1200 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 1004 corresponding to a particular photo mayhave a privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 1000. A privacy setting may bespecified for one or more edges 1006 or edge-types of the social graph1000, or with respect to one or more nodes 1002, 1004 or node-types ofthe social graph 1000. The privacy settings applied to a particular edge1006 connecting two nodes may control whether the relationship betweenthe two entities corresponding to the nodes is visible to other users ofthe online social network. Similarly, the privacy settings applied to aparticular node may control whether the user or concept corresponding tothe node is visible to other users of the online social network. As anexample and not by way of limitation, a first user may share an objectto the social-networking system 160. The object may be associated with aconcept node 1004 connected to a user node 1002 of the first user by anedge 1006. The first user may specify privacy settings that apply to aparticular edge 1006 connecting to the concept node 1004 of the object,or may specify privacy settings that apply to all edges 1006 connectingto the concept node 1004. As another example and not by way oflimitation, the first user may share a set of objects of a particularobject-type (e.g., a set of images). The first user may specify privacysettings with respect to all objects associated with the first user ofthat particular object-type as having a particular privacy setting(e.g., specifying that all images posted by the first user are visibleonly to friends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such image may be used only for alimited purpose (e.g., authentication, tagging the user in photos), andfurther specify that such image may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160.

Systems and Methods

FIG. 13 illustrates an example computer system 1300. In particularembodiments, one or more computer systems 1300 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1300 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1300 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1300.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1300. This disclosure contemplates computer system 1300 taking anysuitable physical form. As example and not by way of limitation,computer system 1300 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1300 may include one or more computersystems 1300; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1300 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1300 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1300 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1300 includes a processor1302, memory 1304, storage 1306, an input/output (I/O) interface 1308, acommunication interface 1310, and a bus 1312. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1302 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1302 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1304, or storage 1306; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1304, or storage 1306. In particularembodiments, processor 1302 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1302 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1302 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1304 or storage 1306, and the instruction caches may speed upretrieval of those instructions by processor 1302. Data in the datacaches may be copies of data in memory 1304 or storage 1306 forinstructions executing at processor 1302 to operate on; the results ofprevious instructions executed at processor 1302 for access bysubsequent instructions executing at processor 1302 or for writing tomemory 1304 or storage 1306; or other suitable data. The data caches mayspeed up read or write operations by processor 1302. The TLBs may speedup virtual-address translation for processor 1302. In particularembodiments, processor 1302 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1302 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1302 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1302. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1304 includes main memory for storinginstructions for processor 1302 to execute or data for processor 1302 tooperate on. As an example and not by way of limitation, computer system1300 may load instructions from storage 1306 or another source (such as,for example, another computer system 1300) to memory 1304. Processor1302 may then load the instructions from memory 1304 to an internalregister or internal cache. To execute the instructions, processor 1302may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1302 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1302 may then write one or more of those results to memory 1304. Inparticular embodiments, processor 1302 executes only instructions in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1302 to memory 1304. Bus 1312 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1302 and memory 1304and facilitate accesses to memory 1304 requested by processor 1302. Inparticular embodiments, memory 1304 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1304 may include one ormore memories 1304, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1306 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1306 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1306 may include removable or non-removable (or fixed)media, where appropriate. Storage 1306 may be internal or external tocomputer system 1300, where appropriate. In particular embodiments,storage 1306 is non-volatile, solid-state memory. In particularembodiments, storage 1306 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1306taking any suitable physical form. Storage 1306 may include one or morestorage control units facilitating communication between processor 1302and storage 1306, where appropriate. Where appropriate, storage 1306 mayinclude one or more storages 1306. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1308 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1300 and one or more I/O devices. Computersystem 1300 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1300. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1308 for them. Where appropriate, I/Ointerface 1308 may include one or more device or software driversenabling processor 1302 to drive one or more of these I/O devices. I/Ointerface 1308 may include one or more I/O interfaces 1308, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1310 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1300 and one or more other computer systems 1300 or oneor more networks. As an example and not by way of limitation,communication interface 1310 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1310 for it. As an example and not by way oflimitation, computer system 1300 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1300 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1300 may include any suitable communicationinterface 1310 for any of these networks, where appropriate.Communication interface 1310 may include one or more communicationinterfaces 1310, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1312 includes hardware, software, or bothcoupling components of computer system 1300 to each other. As an exampleand not by way of limitation, bus 1312 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1312may include one or more buses 1312, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

1. A method comprising: detecting, by a client system associated with afirst user, a user input comprising an incomplete gesture performed byone or more hands of the first user; selecting, by the client systembased on a personalized gesture-recognition model, one or more candidategestures from a plurality of pre-defined gestures, wherein each of thecandidate gestures is associated with a confidence score representing alikelihood the first user intended to input the respective candidategesture; and presenting, at the client system, one or more suggestedinputs corresponding to one or more of the candidate gestures.
 2. Themethod of claim 1, further comprising: calculating, by the client systemfor each of the one or more candidate gestures, a similarity level ofthe candidate gesture with respect to the incomplete gesture.
 3. Themethod of claim 2, wherein the similarly level of each candidate gesturewith respect to the incomplete gesture is based on a trajectory of theincomplete gesture with respect to the client system.
 4. The method ofclaim 2, wherein the similarly level of each candidate gesture withrespect to the incomplete gesture is based on an orientation of theincomplete gesture with respect to the client system.
 5. The method ofclaim 2, wherein the similarly level of each candidate gesture withrespect to the incomplete gesture is based on an object associated withthe incomplete gesture.
 6. The method of claim 2, wherein the similarlylevel of each candidate gesture with respect to the incomplete gestureis based on contextual information associated with the incompletegesture.
 7. The method of claim 2, wherein the similarly level of eachcandidate gesture with respect to the incomplete gesture is based on aposition of the incomplete gesture with respect to the client system. 8.The method of claim 1, further comprising: calculating, by the clientsystem, one or more confidence scores for one or more intentscorresponding to the incomplete gesture; and determining, by the clientsystem, that each of the one or more confidence scores is below athreshold score.
 9. The method of claim 8, wherein the threshold scoreis based on a wake-up gesture performed by the first user.
 10. Themethod of claim 8, wherein calculating the one or more confidence scoresfor the one or more intents corresponding to the incomplete gesture isbased on a velocity associated with the incomplete gesture.
 11. Themethod of claim 8, wherein calculating the one or more confidence scoresfor the one or more intents corresponding to the incomplete gesture isbased on temporal information associated with the incomplete gesture,and wherein the temporal information comprises a pause in the userinput.
 12. The method of claim 8, wherein selecting the one or morecandidate gestures is further based on the one or more intents.
 13. Themethod of claim 1, further comprising: receiving, at the client system,a user-selected input from the first user, wherein the user-selectedinput comprises one of the suggested inputs; and executing, by theclient system, one or more tasks based on the user-selected input. 14.The method of claim 1, wherein each pre-defined gesture comprises one ormore of pointing, poking, tapping, waving, or swiping.
 15. The method ofclaim 1, further comprising: receiving, at the client system, a firstuser-selected input from the first user, wherein the first user-selectedinput comprises one of the suggested inputs, and wherein the firstuser-selected input is associated with a first intent; generating, bythe client system based on the first user-selected input, one or moreadditional candidate gestures, wherein each of the one or moreadditional candidate gestures is associated with the first intent;presenting, at the client system, one or more additional suggestedinputs corresponding to one or more of the additional candidategestures; receiving, at the client system, a second user-selected inputfrom the first user, wherein the second user-selected input comprisesone of the additional suggested inputs; and executing, by the clientsystem, one or more tasks based on the second user-selected input. 16.One or more computer-readable non-transitory storage media embodyingsoftware that is operable when executed to: detect, by a client systemassociated with a first user, a user input comprising an incompletegesture performed by one or more hands of the first user; select, by theclient system based on a personalized gesture-recognition model, one ormore candidate gestures from a plurality of pre-defined gestures,wherein each of the candidate gestures is associated with a confidencescore representing a likelihood the first user intended to input therespective candidate gesture; and present, at the client system, one ormore suggested inputs corresponding to one or more of the candidategestures.
 17. The media of claim 16, wherein the software is furtheroperable when executed to: calculate, by the client system for each ofthe one or more candidate gestures, a similarity level of the candidategesture with respect to the incomplete gesture.
 18. The media of claim17, wherein the similarly level of each candidate gesture with respectto the incomplete gesture is based on a trajectory of the incompletegesture with respect to the client system.
 19. The media of claim 17,wherein the similarly level of each candidate gesture with respect tothe incomplete gesture is based on an orientation of the incompletegesture with respect to the client system.
 20. A system comprising: oneor more processors; and a non-transitory memory coupled to theprocessors comprising instructions executable by the processors, theprocessors operable when executing the instructions to: detect, by aclient system associated with a first user, a user input comprising anincomplete gesture performed by one or more hands of the first user;select, by the client system based on a personalized gesture-recognitionmodel, one or more candidate gestures from a plurality of pre-definedgestures, wherein each of the candidate gestures is associated with aconfidence score representing a likelihood the first user intended toinput the respective candidate gesture; and present, at the clientsystem, one or more suggested inputs corresponding to one or more of thecandidate gestures.